arXiv:2508.19819v3 Announce Type: replace-cross
Abstract: Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly str...
By Viktor Valadi, Lucas Beerens, Mattias {\AA}kesson, Johan \"Ostman, Fazeleh Hoseini, Salman Toor, Andreas Hellander
arXiv:2606. 00986v1 Announce Type: new Abstract: Federated learning (FL) enables multiple data holders to train machine learning models collaboratively without centralizing raw data, making it useful in privacy sensitive domains such as healthcare and institutional data sharing.
By Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi, Teddy Lazebnik
The paper introduces PI‑SME, a Path‑Integral Surrogate Model Extension, to improve gradient inversion attacks in federated learning. By treating a client’s accumulated update as a path integral of the gradient field and approximating it with Gauss–Legendre quadrature over a learnable Bézier path, PI‑SME reconstructs private input images more accurately than existing surrogate baselines. Experiments on CIFAR‑100 and FEMNIST demonstrate its superior performance across various trajectory lengths and class‑restricted batches.
By Agnivo Ghosh, Saumik Bhattacharya
The paper investigates privacy and integrity risks in federated fine‑tuning of a pretrained MobileNetV4 model on Quick, Draw! data. It shows that a single client update can reveal private labels, that label‑count recovery degrades with more clients, and that backdoor and label‑flipping attacks can succeed under FedAvg but are mitigated by FLTrust. The study also demonstrates a live poisoning session using Indigma’s FedMaestro orchestrator.
By Vasileios Perifanis, Nikolaos Pavlidis
Aegis is a client‑side defense for medical federated learning that protects against model inversion attacks by adding a masking gradient derived from locally synthesized data. The method exploits the fact that attacks fail when the effective batch size exceeds the model’s leakage capacity, turning this bottleneck into a privacy guarantee. Experiments on MNIST, CIFAR‑10, and MedMNIST datasets show that Aegis neutralizes state‑of‑the‑art attacks while preserving model accuracy and adding only modest overhead.
By Chaoyu Zhang, Shanghao Shi, Heng Jin, Ning Wang, Y. Thomas Hou, Wenjing Lou
arXiv:2607. 07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself.
By Chongkai Li, Bang Zhang, Wenjian Luo